FFPP-Raw_1FPS_faces-expand-0-aligned_augmentation-normalize-image-mean-std

This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

• Loss: 0.0034
• Accuracy: 0.9983
• Recall: 0.9929
• Precision: 0.9994
• F1: 0.9961
• Roc Auc: 1.0000

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 64
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Recall	Precision	F1	Roc Auc
0.1054	1.0	1377	0.0750	0.9716	0.9180	0.9495	0.9335	0.9957
0.0785	2.0	2755	0.0406	0.9853	0.9596	0.9723	0.9660	0.9986
0.0713	3.0	4132	0.0348	0.9878	0.9534	0.9899	0.9713	0.9994
0.0447	4.0	5510	0.0172	0.9933	0.9842	0.9851	0.9846	0.9997
0.0388	5.0	6887	0.0186	0.9936	0.9741	0.9964	0.9851	0.9998
0.0236	6.0	8265	0.0119	0.9957	0.9830	0.9971	0.9900	0.9999
0.031	7.0	9642	0.0137	0.9957	0.9928	0.9873	0.9900	0.9999
0.015	8.0	11020	0.0072	0.9972	0.9903	0.9969	0.9936	1.0000
0.0429	9.0	12397	0.0087	0.9967	0.9863	0.9987	0.9925	0.9999
0.0186	10.0	13775	0.0052	0.9979	0.9919	0.9985	0.9952	1.0000
0.0282	11.0	15152	0.0069	0.9974	0.9892	0.9988	0.9940	1.0000
0.0034	12.0	16530	0.0045	0.9979	0.9947	0.9956	0.9951	1.0000
0.0187	13.0	17907	0.0070	0.9972	0.9886	0.9986	0.9935	1.0000
0.0136	14.0	19285	0.0038	0.9982	0.9931	0.9988	0.9959	1.0000
0.006	15.0	20662	0.0039	0.9982	0.9928	0.9988	0.9958	1.0000
0.0067	16.0	22040	0.0037	0.9983	0.9926	0.9995	0.9960	1.0000
0.0121	17.0	23417	0.0036	0.9983	0.9929	0.9992	0.9960	1.0000
0.0026	18.0	24795	0.0037	0.9982	0.9925	0.9993	0.9959	1.0000
0.0024	19.0	26172	0.0034	0.9983	0.9932	0.9991	0.9961	1.0000
0.002	19.99	27540	0.0034	0.9983	0.9929	0.9994	0.9961	1.0000

Framework versions

• Transformers 4.39.2
• Pytorch 2.2.2
• Datasets 2.18.0
• Tokenizers 0.15.2